An Improving Tabu Search Algorithm for Intrusion Detection

Utilizing feature selection in intrusion detection can remove redundant features and improve the speed of the intrusion detection system efficiently on the basis of protecting the integrity of the original data. This paper proposes a new feature selection method that is based on KNN and Tabu search algorithm. The experiment result shows that this method can remove the redundant features, and reduce the time of feature selection. This method not only guarantees the accuracy of detection but also improves the detection speed efficiently.